#!/usr/bin/env python
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

# /// script
# dependencies = [
#     "transformers @ git+https://github.com/huggingface/transformers.git",
#     "datasets >= 2.0.0",
#     "torch >= 1.3",
#     "accelerate",
#     "evaluate""
#     "Pillow",
#     "albumentations >= 1.4.16",
# ]
# ///

import json
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from functools import partial
from typing import Optional

import albumentations as A
import evaluate
import numpy as np
import torch
from albumentations.pytorch import ToTensorV2
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from torch import nn

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


""" Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation leveraging the Trainer API."""

logger = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.57.0.dev0")

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")


def reduce_labels_transform(labels: np.ndarray, **kwargs) -> np.ndarray:
    """Set `0` label as with value 255 and then reduce all other labels by 1.

    Example:
        Initial class labels:         0 - background; 1 - road; 2 - car;
        Transformed class labels:   255 - background; 0 - road; 1 - car;

    **kwargs are required to use this function with albumentations.
    """
    labels[labels == 0] = 255
    labels = labels - 1
    labels[labels == 254] = 255
    return labels


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify
    them on the command line.
    """

    dataset_name: Optional[str] = field(
        default="segments/sidewalk-semantic",
        metadata={
            "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
        },
    )
    dataset_config_name: Optional[str] = field(
        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
    )
    train_val_split: Optional[float] = field(
        default=0.15, metadata={"help": "Percent to split off of train for validation."}
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        },
    )
    do_reduce_labels: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."},
    )
    reduce_labels: Optional[bool] = field(
        default=False,
        metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."},
    )

    def __post_init__(self):
        if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
            raise ValueError(
                "You must specify either a dataset name from the hub or a train and/or validation directory."
            )
        if self.reduce_labels:
            self.do_reduce_labels = self.reduce_labels
            warnings.warn(
                "The `reduce_labels` argument is deprecated and will be removed in v4.45. Please use `do_reduce_labels` instead.",
                FutureWarning,
            )


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        default="nvidia/mit-b0",
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
    )
    config_name: Optional[str] = field(
        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
    )
    cache_dir: Optional[str] = field(
        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
    token: str = field(
        default=None,
        metadata={
            "help": (
                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
                "generated when running `hf auth login` (stored in `~/.huggingface`)."
            )
        },
    )
    trust_remote_code: bool = field(
        default=False,
        metadata={
            "help": (
                "Whether to trust the execution of code from datasets/models defined on the Hub."
                " This option should only be set to `True` for repositories you trust and in which you have read the"
                " code, as it will execute code present on the Hub on your local machine."
            )
        },
    )


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
    dataset = load_dataset(
        data_args.dataset_name, cache_dir=model_args.cache_dir, trust_remote_code=model_args.trust_remote_code
    )

    # Rename column names to standardized names (only "image" and "label" need to be present)
    if "pixel_values" in dataset["train"].column_names:
        dataset = dataset.rename_columns({"pixel_values": "image"})
    if "annotation" in dataset["train"].column_names:
        dataset = dataset.rename_columns({"annotation": "label"})

    # If we don't have a validation split, split off a percentage of train as validation.
    data_args.train_val_split = None if "validation" in dataset else data_args.train_val_split
    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
        split = dataset["train"].train_test_split(data_args.train_val_split)
        dataset["train"] = split["train"]
        dataset["validation"] = split["test"]

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    if data_args.dataset_name == "scene_parse_150":
        repo_id = "huggingface/label-files"
        filename = "ade20k-id2label.json"
    else:
        repo_id = data_args.dataset_name
        filename = "id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset")))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: str(k) for k, v in id2label.items()}

    # Load the mean IoU metric from the evaluate package
    metric = evaluate.load("mean_iou", cache_dir=model_args.cache_dir)

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    @torch.no_grad()
    def compute_metrics(eval_pred):
        logits, labels = eval_pred
        logits_tensor = torch.from_numpy(logits)
        # scale the logits to the size of the label
        logits_tensor = nn.functional.interpolate(
            logits_tensor,
            size=labels.shape[-2:],
            mode="bilinear",
            align_corners=False,
        ).argmax(dim=1)

        pred_labels = logits_tensor.detach().cpu().numpy()
        metrics = metric.compute(
            predictions=pred_labels,
            references=labels,
            num_labels=len(id2label),
            ignore_index=0,
            reduce_labels=image_processor.do_reduce_labels,
        )
        # add per category metrics as individual key-value pairs
        per_category_accuracy = metrics.pop("per_category_accuracy").tolist()
        per_category_iou = metrics.pop("per_category_iou").tolist()

        metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)})
        metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)})

        return metrics

    config = AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path,
        label2id=label2id,
        id2label=id2label,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    model = AutoModelForSemanticSegmentation.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )
    image_processor = AutoImageProcessor.from_pretrained(
        model_args.image_processor_name or model_args.model_name_or_path,
        do_reduce_labels=data_args.do_reduce_labels,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        token=model_args.token,
        trust_remote_code=model_args.trust_remote_code,
    )

    # Define transforms to be applied to each image and target.
    if "shortest_edge" in image_processor.size:
        # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
        height, width = image_processor.size["shortest_edge"], image_processor.size["shortest_edge"]
    else:
        height, width = image_processor.size["height"], image_processor.size["width"]
    train_transforms = A.Compose(
        [
            A.Lambda(
                name="reduce_labels",
                mask=reduce_labels_transform if data_args.do_reduce_labels else None,
                p=1.0,
            ),
            # pad image with 255, because it is ignored by loss
            A.PadIfNeeded(min_height=height, min_width=width, border_mode=0, value=255, p=1.0),
            A.RandomCrop(height=height, width=width, p=1.0),
            A.HorizontalFlip(p=0.5),
            A.Normalize(mean=image_processor.image_mean, std=image_processor.image_std, max_pixel_value=255.0, p=1.0),
            ToTensorV2(),
        ]
    )
    val_transforms = A.Compose(
        [
            A.Lambda(
                name="reduce_labels",
                mask=reduce_labels_transform if data_args.do_reduce_labels else None,
                p=1.0,
            ),
            A.Resize(height=height, width=width, p=1.0),
            A.Normalize(mean=image_processor.image_mean, std=image_processor.image_std, max_pixel_value=255.0, p=1.0),
            ToTensorV2(),
        ]
    )

    def preprocess_batch(example_batch, transforms: A.Compose):
        pixel_values = []
        labels = []
        for image, target in zip(example_batch["image"], example_batch["label"]):
            transformed = transforms(image=np.array(image.convert("RGB")), mask=np.array(target))
            pixel_values.append(transformed["image"])
            labels.append(transformed["mask"])

        encoding = {}
        encoding["pixel_values"] = torch.stack(pixel_values).to(torch.float)
        encoding["labels"] = torch.stack(labels).to(torch.long)

        return encoding

    # Preprocess function for dataset should have only one argument,
    # so we use partial to pass the transforms
    preprocess_train_batch_fn = partial(preprocess_batch, transforms=train_transforms)
    preprocess_val_batch_fn = partial(preprocess_batch, transforms=val_transforms)

    if training_args.do_train:
        if "train" not in dataset:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            dataset["train"] = (
                dataset["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
            )
        # Set the training transforms
        dataset["train"].set_transform(preprocess_train_batch_fn)

    if training_args.do_eval:
        if "validation" not in dataset:
            raise ValueError("--do_eval requires a validation dataset")
        if data_args.max_eval_samples is not None:
            dataset["validation"] = (
                dataset["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
            )
        # Set the validation transforms
        dataset["validation"].set_transform(preprocess_val_batch_fn)

    # Initialize our trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset["train"] if training_args.do_train else None,
        eval_dataset=dataset["validation"] if training_args.do_eval else None,
        compute_metrics=compute_metrics,
        processing_class=image_processor,
        data_collator=default_data_collator,
    )

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Write model card and (optionally) push to hub
    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "dataset": data_args.dataset_name,
        "tags": ["image-segmentation", "vision"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)


if __name__ == "__main__":
    main()
